Detection of Heart Failure Using a Convolutional Neural Network via ECG Signals

Jad Botros (1) , Farah Mourad-Chehade (1) , David Laplanche (2, 1)

1 LOSI - Laboratoire d'Optimisation des Systèmes Industriels
2 Centre hospitalier de Troyes

Heart failure (HF) is a chronic heart condition that increases mortality, morbidity, and healthcare costs. The electrocardiogram (ECG) is a noninvasive and straightforward diagnostic tool that can reveal detectable changes in HF. Because of their small amplitude and duration, these changes can be subtle and potentially misclassified during manual interpretation or when analyzed by clinicians. This paper reports a 7 -layer deep convolutional neural network (CNN) model for HF automatic detection. The proposed CNN model requires only minimal pre-processing of ECG signals and does not require any engineered features. The model is trained and tested using an unbalanced and a balanced datasets extracted from the MIT-BIH and the BIDMC databases, achieving an accuracy of 99.73%, a sensitivity of 99.58%, and a specificity of 99.83% when the dataset is unbalanced and an accuracy of 99.26%, a sensitivity of 99.37%, and a specificity of 99.12% when the dataset is balanced.

Keywords: Binary Classification; Convolutional Neural Network; Deep Learning; Electrocardiogram; Heart Failure

Domains: Computer Science [cs]

Jad Botros, Farah Mourad-Chehade, David Laplanche. Detection of Heart Failure Using a Convolutional Neural Network via ECG Signals. 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Nov 2022, Beijing, China. pp.1-5, ⟨10.1109/CISP-BMEI56279.2022.9980118⟩. ⟨hal-04454678⟩